Multi-Agent Reinforcement Learning for Swarm Retrieval with Evolving Neural Network
AffiliationRoyal Academy of Engineering; University of Chester
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AbstractThis research investigates methods for evolving swarm communica-tion in a sim-ulated colony of ants using pheromone when foriaging for food. This research implemented neuroevolution and obtained the capability to learn phero-mone communication autonomously. Building on previous literature on phero-mone communication, this research applies evolution to adjust the topology and weights of an artificial neural network (ANN) which controls the ant behaviour. Compar-ison of performance is made between a hard-coded benchmark algorithm (BM1), a fixed topology ANN and neuroevolution of the ANN topology and weights. The resulting neuroevolution produced a neural network which was suc-cessfully evolved to achieve the task objective, to collect food and return it to a location.
CitationVaughan N. (2018) Multi-agent Reinforcement Learning for Swarm Retrieval with Evolving Neural Network. In: Vouloutsi V. et al. (eds) Biomimetic and Biohybrid Systems. Living Machines 2018. Lecture Notes in Computer Science, vol 10928. Springer
DescriptionThe final publication is available at Springer via https://doi.org/10.1007/978-3-319-95972-6_56
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/3.0/